This paper examines the current implementation of Artificial Intelligence (AI) in higher education and its implications for inclusivity, particularly for minority groups. Using a rapid review methodology, it synthesises academic literature, policy reports, and case studies to explore how AI is reshaping educational environments. The analysis reveals that although AI technologies—such as adaptive learning systems, intelligent tutoring, and predictive analytics—are increasingly adopted, their primary aim remains institutional efficiency rather than fostering equity. Initiatives explicitly designed to support underrepresented students are rare, exposing a gap between technological innovation and inclusive practice. The study identifies key barriers, including socioeconomic inequality, cultural and linguistic bias, and limited institutional capacity, which are often compounded by AI systems trained on non-representative data. While isolated case studies demonstrate that (e.g., culturally) responsive AI can enhance educational access for marginalised learners, these remain exceptions rather than norms. The findings suggest that without deliberate efforts to embed inclusivity in AI design and deployment, existing inequalities may be perpetuated or worsened. The paper concludes that realising AI’s inclusive potential requires ethical frameworks, diverse development teams, and equitable access strategies. It calls for future empirical research focused on practical interventions that reduce disparities, contributing to a more just and inclusive higher education landscape.
Conceição et al. (2025) studied this question.